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You are right that for basic growth monitoring weight and size are the core measurements and statistical sampling is sufficient. We are actually testing underwater deployment for exactly that use case. Biomass estimation, feeding behavior tracking, population density. Non-contact measurement where the fish stay in the water.

But when you move into broodstock selection the requirements are completely different. It is not just weight and size anymore. Farms care about genotyping, body shape ratios, fin integrity, and overall quality markers that predict how well offspring will perform. You need detailed phenotypic data on individual fish and you want as many individuals as possible to improve selection accuracy. Marine species add another layer of complexity. Deformities are a major problem. Vertebral curvature, operculum malformations, color abnormalities. These are not things you can measure with a camera underwater. Farms are already taking fish out of the water for these inspections and doing it manually. We are not introducing handling where it did not exist. We are helping automate and track what they are already doing by hand.

The goal is to match the approach to the task. Underwater non-contact for biomass and behavior. Controlled environment with handling for broodstock phenotyping and deformity inspection. Different problems require different solutions.

We have not published anything yet but it is on our roadmap. Right now we are heads down on deployment and iterating fast so documentation has taken a backseat. Once we have more data across multiple facilities and species we want to write up what we have learned about calibration strategies for non-controlled environments. There is not a lot of practical literature out there on edge CV in wet and variable conditions so it feels like something worth sharing.

You raise a valid point about feed. It is true that carnivorous species like salmon still have FIFO ratios above 1.0, meaning they consume more wild fish than they produce. That is a real concern for those species.

That said, the feed picture has changed a lot over the past two decades. The overall FIFO ratio for aquaculture dropped from 0.63 in 2000 to around 0.22 to 0.28 by 2017 depending on how you calculate it. Modern feeds are increasingly plant based with soy, barley, and other terrestrial proteins making up the bulk of formulations. Up to 30% of fishmeal now comes from processing byproducts rather than whole wild fish. There is also a lot of work happening on alternative proteins like insect meal, algae, and single cell proteins.

The species matters a lot here. Tilapia, catfish, and carps use very little wild fish in their feed. Salmon and shrimp are the outliers that drive most of the wild fish dependency. We are currently working with trout which sits somewhere in the middle.

On the sentience discussion, I am not going to dismiss people who care about fish welfare. It is a legitimate concern and the science on fish cognition is evolving. Our technology actually reduces stress on fish compared to manual handling which involves netting, anesthesia, and physical manipulation. If people care about welfare then better measurement tools that minimize handling should be part of the solution.

We are usually interacting with anesthetized fish and farms are already doing manual tasks with this. We are also integrating camera's into existing workflow that do not handle the fish to improve a farms insight into their own farms.

There are many tasks within farms that are extremely manual that are a bottleneck, this is especially prevalent in offshore farms and marine species.

they're heavy but we have some post processing tasks related to the smoothness of the countours created by the labels. sometimes CNNs do not have smooth segmentation. We do various calculations to determine deformities or welfare indicators on a fish. For that we need some smoother contours.

Given that, we have trained a variety of models and we are still experimenting on what works the best. We are even considering using VLMs for certain tasks if we can fine-tune them well enough.

thank you, appreciate it

We are doing exactly what you described with continuous calibration. We have essentially built our own in-house labeling, ingesting, and task assignment software for these tasks. Low confidence predictions get flagged for review, corrected labels feed back into training, and we retrain on a rolling basis. We also stratify our calibration datasets intentionally by time of day, tank conditions, and fish density rather than just grabbing random frames. Early on our datasets were too homogenous and the models would work great in testing then degrade in production. The architecture matters less than having a tight feedback loop between deployment and retraining.

On the welfare angle, yes we are thinking about this carefully. The data we collect includes body shape, fin integrity, spinal curvature, and other morphological traits that are signals of fish health and robustness, not just growth rate. Farms that care about sustainability can use this to select for fish that are healthy and resilient rather than just fast growing. The tool is neutral but the selection criteria are up to the breeder. We do not want to enable the same failure mode that happened with poultry.

The talent pipeline point is interesting too. You are right that most CV talent ends up in adtech or fintech. We have found that people get excited about working on something physical and tangible once they realize the problems are just as hard.

Great question. We are specifically targeting vertically integrated farms which are usually massive scale operations with hundreds of thousands to millions of fish. These facilities already have significant labor costs for trained technicians and geneticists doing manual phenotyping and sorting. We are not going after small scale manual farmers or trying to be an eFishery type business that serves smallholders.

The labor dynamics are also different at these large farms. The work we are automating is repetitive, physically demanding, and hard to staff consistently. Most of the farm managers we talk to are not trying to replace people, they are struggling to find enough workers willing to do this work in the first place. Automation at this scale tends to shift jobs rather than eliminate them entirely. And thank you for the kind words on the website! We have gotten mixed reviews on the cursor so enjoy it while it lasts lol

Thank you! Goa has a way of making the ocean feel like part of everyday life. It is hard to grow up there and not think about fishing and seafood at some point.

Would love to connect with your cohort. North East India has a ton of freshwater aquaculture potential and we are always looking to learn from founders working in different geographies and species.

You are right that aqua startups need each other. The industry is so fragmented and underserved that collaboration makes more sense than competition at this stage.

yes it's definitely capital intensive for sure. feedback on the website is noted, will make the changes on the next release.

We are hiring, would love to chat if you have some time.

This is a thoughtful critique and I appreciate you raising it directly. You are right that fish sentience is getting more attention in the literature and it deserves more ethical scrutiny than it currently receives. We do not take the position that scaling aquaculture is straightforwardly good without tradeoffs. There are real welfare concerns and the industry has not always handled them well.

A few thoughts on where we stand:

On welfare specifically, our technology actually reduces stress on fish compared to the current manual process. Traditional phenotyping involves netting, anesthesia, and physical handling. Our system measures fish without any of that. Less handling means lower cortisol, lower mortality, and healthier animals. We are not neutral on welfare. We think better measurement tools should lead to better treatment, not just faster growth.

On plant based protein, you are right that our framing glosses over it. Plant based is more efficient on land use and we are not arguing against it. The reality is that billions of people rely on fish as their primary protein source today and that is not going to change overnight. We are trying to make the aquaculture that already exists more sustainable and humane, not argue that it is the only path forward.

On the scale of killing, I do not have a good answer for that. It is a massive number and I understand why that gives you pause. What I can say is that if aquaculture is going to exist at scale, we would rather it be done with better data, less stress on the animals, and more intentional breeding practices than the status quo.

well these days inference can be done on CPUs depending on the size of a model. the OAK-D camera's especially are literally just computer. They are linux and you can SSH directly into them which is insane. The camera itself is shaped like a heat sink to dissipate heat. it's honestly phenomenal. I talked to the CEO of the company as well and I'm really impressed with their machine and supply chain. so all in all, high hopes for them.

The broader point we were trying to make is that as population grows and arable land decreases, the protein mix will need to shift. Fish have a feed conversion ratio of around 1.0 to 1.5 compared to 6 to 10 for beef. They do not require land for grazing and aquaculture has significantly lower greenhouse gas emissions than most terrestrial livestock. That does not mean aquaculture is the only path forward but it is likely a bigger part of the equation than it is today, especially in regions where seafood is already a primary protein source. We are not arguing against plant based diets or other solutions. Just that aquaculture is underinvested relative to its potential and the infrastructure to scale it efficiently does not exist yet. That is the gap we are trying to fill.

This is a really important point and something we think about a lot. You are absolutely right that chicken optimization has come with serious welfare tradeoffs. Breeding purely for growth rate without considering the animal's ability to actually live comfortably is how you end up with birds that cannot walk properly. We do not want to enable that trajectory for fish.

The good news is that the data we are collecting can be used to select for more than just growth. Body shape, fin integrity, spinal curvature, and other morphological traits are all signals of fish health and welfare. Farms that care about sustainability can use this data to breed fish that are robust and healthy, not just fast growing. The tool is neutral but the selection criteria are up to the breeder.

On the environmental side, our focus right now is on land based hatcheries and recirculating aquaculture systems rather than open net pens in the ocean. These closed systems avoid a lot of the dewilding and pollution concerns you mentioned. They are more expensive to operate but they keep farmed fish separate from wild populations and give you much more control over waste and water quality. And yes, reducing handling stress is a big part of what we are building. The manual process today involves netting, anesthesia, and physical manipulation. Our system can measure fish without any of that. Less stress on the animal and better data for the farmer.

interesting, I like the idea

Thanks! The quantization tradeoffs have been a grind. We do not have an exact number but we found that a few thousand images was not enough once you account for the variance on farm. Lighting changes throughout the day, water clarity shifts between feedings, fish density varies by tank. Early on our calibration sets were too homogenous and the INT8 models would work great in testing and then fall apart when conditions shifted.

We also found that segmentation required significantly fewer images compared to keypoint pose detection models. Segmentation generalizes faster since you are just finding body boundaries. Keypoints are more finicky because anatomical landmarks vary a lot more across species, life stages, and body deformation while swimming. We had to be much more intentional about diversity in the keypoint training data. What made the difference overall was building calibration sets that intentionally captured edge cases. Low light, high turbidity, dense occlusion, different life stages. We also started stratifying by time of day and tank conditions rather than just grabbing random frames. It is still not perfect but the models are much more stable now.

yup I got similar feedback from other batchmatches, gonna fix this.

This is a great comment. You are absolutely right about the data opportunity. The industry is so data sparse right now that even basic measurements at scale would be a step change. We are seeing that firsthand with our customer. They went from sampling a few dozen fish by hand to continuous measurement and the insights are already compounding.

Thank you for the fish pump link. We have looked at pump based systems as a way to create controlled measurement environments. You get consistent lighting, predictable fish orientation, and the fish are already moving through a constrained path. The challenge is you are still dealing with water turbidity, particulates, and bubbles in the flow which can mess with imaging. It is better than open water but not a free pass on the vision problems.

We have also been looking at pescalators which use an Archimedes screw design to lift fish out of the water. Some setups combine this with anesthetization for operations that require handling. The tradeoff is you are adding stress and complexity but you get a much cleaner imaging environment. There is no single right answer here and the best approach depends on the species, life stage, and what you are trying to measure. This is definitely technology that will develop over time as the industry matures.

What species are you working with in your aquaponics setup?

Great question. We are building our entire labeling and data management system in house. Early on we tried existing platforms but they did not fit our workflow. We have a lot of video data and need custom labeling for things like keypoints, body outlines, and deformity classification that off the shelf tools do not handle well. Building it ourselves is cheaper at our scale, gives us tighter integration between labeling, training pipelines, and deployment, and lets us iterate faster. We can assign tasks to annotators, version datasets, and push models to edge devices from one system. When you are trying to close the loop between data collection on farm and deployment you cannot afford fragmented tooling.

We got a little too excited about the fish theme. Noted for the next iteration.

Thanks! Yes turbidity has been one of our bigger challenges. Water clarity can shift dramatically throughout the day depending on feeding, fish activity, and weather. We have had to build our calibration datasets to capture that variance otherwise the quantized models degrade fast in production. We are also experimenting with different lighting setups to cut through particulate but it is still a work in progress :)

Ha, thank you. We figured if we are building robots for fish we might as well commit to the bit. Enjoy it while it lasts.